Difference between revisions of "Spring 2017 CS292F Syllabus"
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** [http://www.bioinf.jku.at/publications/older/2604.pdf Long short term memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 1997] | ** [http://www.bioinf.jku.at/publications/older/2604.pdf Long short term memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 1997] | ||
** [https://arxiv.org/pdf/1409.1259.pdf On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, Cho et al., 2014] | ** [https://arxiv.org/pdf/1409.1259.pdf On the Properties of Neural Machine Translation: Encoder–Decoder Approaches, Cho et al., 2014] | ||
| − | *05/02 | + | *05/02 Sequence-to-sequence models and neural machine translation |
| + | ** [https://arxiv.org/pdf/1406.1078.pdf Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Cho et al., EMNLP 2014] | ||
| + | ** [https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Sequence to Sequence Learning with Neural Networks, Sutskever et al., NIPS 2014] | ||
| + | *05/04 Attention mechanisms in NLP | ||
| + | *05/09 Project: mid-term presentation (1) | ||
| + | *05/11 Project: mid-term presentation (2) (HW2 due) | ||
| + | *05/16 Convolutional Neural Networks | ||
** [http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011] | ** [http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011] | ||
** [http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf Convolutional Neural Networks for Sentence Classification, Yoon Kim, EMNLP 2014] | ** [http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf Convolutional Neural Networks for Sentence Classification, Yoon Kim, EMNLP 2014] | ||
| − | *05/ | + | *05/18 Language and vision |
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** [https://arxiv.org/pdf/1411.4555.pdf Show and Tell: A Neural Image Caption Generator, CVPR 2015] | ** [https://arxiv.org/pdf/1411.4555.pdf Show and Tell: A Neural Image Caption Generator, CVPR 2015] | ||
** [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.pdf Deep Visual-Semantic Alignments for Generating Image Descriptions, Andrej Karpathy and Li Fei-Fei, CVPR 2015] | ** [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.pdf Deep Visual-Semantic Alignments for Generating Image Descriptions, Andrej Karpathy and Li Fei-Fei, CVPR 2015] | ||
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*05/23 Speech recognition and understanding | *05/23 Speech recognition and understanding | ||
** [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/HintonDengYuEtAl-SPM2012.pdf Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, Hinton et al., 2012 IEEE Signal Proc. Magazine] | ** [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/HintonDengYuEtAl-SPM2012.pdf Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, Hinton et al., 2012 IEEE Signal Proc. Magazine] | ||
** [https://www.cs.toronto.edu/~gdahl/papers/DBN4LVCSR-TransASLP.pdf Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition, Dahl et al., 2012 IEEE TASLP] | ** [https://www.cs.toronto.edu/~gdahl/papers/DBN4LVCSR-TransASLP.pdf Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition, Dahl et al., 2012 IEEE TASLP] | ||
| − | *05/25 | + | *05/25 Information Extraction |
| − | ** [https://arxiv.org/pdf/ | + | *05/30 Summarization |
| − | + | ** [https://arxiv.org/pdf/1509.00685.pdf A neural attention model for abstractive sentence summarization EMNLP 2015] | |
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*06/01 Question answering | *06/01 Question answering | ||
*06/06 Project: final presentation (1) | *06/06 Project: final presentation (1) | ||
*06/08 Project: final presentation (2) | *06/08 Project: final presentation (2) | ||
Revision as of 22:14, 25 March 2017
- 04/04 Introduction, logistics, NLP, and deep learning.
- 04/06 Tips for a successful class project
- 04/11 Word embeddings (HW1 out)
- A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Collobert and Weston, ICML 2008
- Distributed Representations of Words and Phrases and their Compositionality, T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean, NIPS 2013
- Glove: Global Vectors for Word Representation, J Pennington, R Socher, CD Manning - EMNLP, 2014
- 04/13 Knowledge base embeddings
- A three-way model for collective learning on multi-relational data, M Nickel, V Tresp, HP Kriegel, ICML 2011
- Translating embeddings for modeling multi-relational data, A Bordes, N Usunier, A Garcia-Duran, NIPS 2013
- A Review of Relational Machine Learning for Knowledge Graphs, Nichel et al., Proceedings of the IEEE
- 04/18 Neural network basics (Project proposal due)
- 04/20 Neural networks language models
- 04/25 RNNs (HW1 due and HW2 out)
- 04/27 LSTMs/GRUs
- 05/02 Sequence-to-sequence models and neural machine translation
- 05/04 Attention mechanisms in NLP
- 05/09 Project: mid-term presentation (1)
- 05/11 Project: mid-term presentation (2) (HW2 due)
- 05/16 Convolutional Neural Networks
- 05/18 Language and vision
- 05/23 Speech recognition and understanding
- 05/25 Information Extraction
- 05/30 Summarization
- 06/01 Question answering
- 06/06 Project: final presentation (1)
- 06/08 Project: final presentation (2)
